Does AI in Digital Healthcare Experiences Remove or Add Friction?

Artificial Intelligence (AI) is reshaping industries, with healthcare at the forefront of this revolution. But while the promise of AI in healthcare is immense, does it ease friction for organizations and consumers - or adds new challenges?

On a recent episode of The Frictionless Experience, Dr. Tina Manoharan, former VP and Global Leader of Data/AI & Digital Innovation at Philips, explores how AI impacts healthcare delivery, overcoming challenges in AI adoption, her patient-centric approach to AI development, and shares a compelling vision for the future of healthcare tech.

As we think about the role that AI plays in healthcare, below are 3 advantages in which AI can remove friction for healthcare providers and patients.

1. Easing Diagnostic Friction with AI

One of the most significant benefits of AI in healthcare is its potential to aid in precise and timely diagnoses. Dr. Manoharan highlights how AI-driven tools can assist radiologists, cardiologists, and other specialists by "extracting relevant information from images," ensuring that patients receive accurate diagnoses without over- or under-treatment.

She elaborates, "AI is assisting already and saying, look, there's a change there, and pointing in the right direction."

AI-driven diagnostic tools can be especially useful for detecting subtle changes that might go unnoticed by the human eye. This early intervention capability is invaluable, as early detection often translates into better patient outcomes.

2. Removing Administrative Burden

One of the main friction points in healthcare is the administrative burden that clinicians face.

AI can streamline many of these processes, allowing healthcare professionals to focus on patient care rather than paperwork. As Dr. Manoharan explains, AI can "automate some of the processes like creating a documentation summary after a procedure."

By alleviating such tasks, clinicians can devote more time to patient interaction and less to documentation.

3. Personalizing Patient Journeys

AI also plays a crucial role in personalizing patient journeys, a key element in reducing friction between healthcare providers and patients. Patients want quicker, more personalized care, and AI can assist in this area. Dr. Manoharan says, "For instance, getting their reports and results right away after a scan on their mobile devices".

This immediate access to information can alleviate the anxiety patients often feel while waiting for test results.

Moreover, AI can empower patients by giving them the tools to monitor their health more effectively, like sleep apnea devices or home-based monitors. This leads to more informed decisions and a feeling of control over their healthcare journey.

Dr. Manoharan notes:

Ultimately it's about empowering individuals. Today you have all your bank details at your fingertips on your mobile device and you're able to do everything there. But it's not the same with healthcare. You don't have your holistic information at your fingertips, and that would be great."

Frictionless digital healthcare experiences empower patients to take an active role in managing their own care, which benefits patients and the healthcare industry as a whole. 

Now, let's consider the disadvantages of AI in healthcare, in which it can introduce friction for healthcare providers and patients.

1. Regulatory and Ethical Challenges

Despite these advantages, implementing AI in healthcare comes with its own set of challenges. Regulatory and privacy concerns, in particular, can create friction.

Dr. Manoharan points out that different countries have varied regulations that impact how AI is used. She says, "The regulations are not settled... even the regulatory bodies are learning."

This uncertainty can slow down the integration of AI technologies and make it challenging to deploy solutions globally.

Another challenge is data privacy, especially when dealing with sensitive health information.

AI systems must be designed to ensure compliance with data protection regulations, like the EU's AI Act and the FDA's guidelines. Without robust data governance, deploying AI could introduce more risks than benefits.

2. AI Bias and Global Applicability

AI systems are only as good as the data they are trained on, which can lead to potential bias in healthcare outcomes.

For instance, Dr. Manoharan notes that "data is different by countries" and cannot be universally applied without customization.

She says:

"So you cannot say, I trained the AI model on patients in the US, and I'm gonna start deploying in the Asian market, because even as simple as the organ sizes are different, the individuals height, weight, and so on, they are different. So you really need to understand the clinical scenario, retrain the models as appropriate, and hence, you need a very good data and AI strategy to start with before you start saying, okay, I have data or let's create an innovative AI model."

Such variations highlight the need for localized data sets and AI systems capable of adapting to specific populations, adding friction to AI deployment on a global scale.

The Future of AI in Healthcare: A Co-Creation Approach

One of the key takeaways from Dr. Manoharan's insights is the importance of a co-creation approach when designing AI solutions for healthcare. AI cannot operate in a vacuum; it must be developed alongside healthcare professionals to ensure it aligns with clinical workflows.

She emphasizes:

"You really need to start with, do I understand the need? Do I understand what decision the clinician is trying to make at that point?"

Without this collaborative approach, even the most advanced AI solutions risk adding friction rather than reducing it.

So, Does AI in Digital Healthcare Experiences Remove or Add Friction?

AI is a powerful tool that has the potential to dramatically reduce friction in healthcare, from diagnostic improvements to streamlined administrative processes. However, it also introduces new challenges, including regulatory hurdles, data privacy concerns, and the risk of bias.

The key to success lies in careful, collaborative development, ensuring that AI solutions truly meet the needs of clinicians and patients alike. As Dr. Manoharan aptly puts it, "AI is not about hindering them in doing their routine clinical workflow... it should be well integrated."

AI has the potential to make healthcare more frictionless, but only if it is applied thoughtfully and with a deep understanding of the complexities involved.